[1] Alexander, G. D., J. A. Weinman, V. M. Karyampudi, et al., 1999: The effect of assimilating rain rates derived from satellites and lightning on forecasts of the 1993 superstorm. Mon. Wea. Rev., 127, 1433–1457. doi: 10.1175/1520-0493(1999)127<1433:TEOARR>2.0.CO;2
[2] Cha, J.-W., K.-H. Chang, S. S. Yum, et al., 2009: Comparison of the bright band characteristics measured by Micro Rain Radar (MRR) at a mountain and a coastal site in South Korea. Adv. Atmos. Sci., 26, 211–221. doi: 10.1007/s00376-009-0211-0
[3] Chang, D.-E., J. A. Weinman, C. A. Morales, et al., 2001: The effect of spaceborne microwave and ground-based continuous lightning measurements on forecasts of the 1998 groundhog day storm. Mon. Wea. Rev., 129, 1809–1833. doi: 10.1175/1520-0493(2001)129<1809:TEOSMA>2.0.CO;2
[4] Chen, F., Z. Janjić, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound.-Layer Meteor., 85, 391–421. doi: 10.1023/A:1000531001463
[5] Chen, G. Z., T. J. Wang, S. S. Lou, et al., 2014: Analysis on the distributions and instabilities of lightning in Anhui. J. Meteor. Sci., 34, 543–548. (in Chinese) doi: 10.3969/2013jms.0012
[6] Chen, J.-H., Q. Zhang, W.-X. Feng, et al., 2008: Lightning location system and lightning detection network of China. High Voltage Engineering, 34, 425–431. (in Chinese) doi: 10.13336/j.1003-6520.hve.2008.03.020
[7] Chen, Z. X., X. S. Qie, D. X. Liu, et al., 2019: Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model. Atmos. Res., 221, 72–87. doi: 10.1016/j.atmosres.2019.02.001
[8] Federico, S., E. Avolio, M. Petracca, et al., 2014: Simulating lightning into the RAMS model: Implementation and preliminary results. Nat. Hazards Earth Syst. Sci., 14, 2933–2950. doi: 10.5194/nhess-14-2933-2014
[9] Federico, S., M. Petracca, G. Panegrossi, et al., 2017a: Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation. Nat. Hazards Earth. Syst. Sci., 17, 61–76. doi: 10.5194/nhess-17-61-2017
[10] Federico, S., M. Petracca, G. Panegrossi, et al., 2017b: Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges. Adv. Sci. Res., 14, 187–194. doi: 10.5194/asr-14-187-2017
[11] Federico, S., R. C. Torcasio, E. Avolio, et al., 2019: The impact of lightning and radar reflectivity factor data assimilation on the very short-term rainfall forecasts of RAMS@ISAC: Application to two case studies in Italy. Nat. Hazards Earth Syst. Sci., 19, 1839–1864. doi: 10.5194/nhess-19-1839-2019
[12] Fierro, A. O., E. R. Mansell, C. L. Ziegler, et al., 2012: Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011. Mon. Wea. Rev., 140, 2609–2627. doi: 10.1175/MWR-D-11-00299.1
[13] Fierro, A. O., A. J. Clark, E. R. Mansell, et al., 2015: Impact of storm-scale lightning data assimilation on WRF-ARW precipitation forecasts during the 2013 warm season over the contiguous United States. Mon. Wea. Rev., 143, 757–777. doi: 10.1175/MWR-D-14-00183.1
[14] Fierro, A. O., J. D. Gao, C. L. Ziegler, et al., 2016: Assimilation of flash extent data in the variational framework at convection-allowing scales: Proof-of-concept and evaluation for the short-term forecast of the 24 May 2011 tornado outbreak. Mon. Wea. Rev., 144, 4373–4393. doi: 10.1175/MWR-D-16-0053.1
[15] Gao, J. D., M. Xue, K. Brewster, et al., 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457–469. doi: 10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2
[16] Goines, D. C., and A. D. Kennedy, 2018: Precipitation from a multiyear database of convection-allowing WRF simulations. J. Geophys. Res. Atmos., 123, 2424–2453. doi: 10.1002/2016JD026068
[17] Haase, G., S. Crewell, C. Simmer, et al., 2000: Assimilation of radar data in mesoscale models: Physical initialization and latent heat nudging. Phys. Chem. Earth, Part B, 25, 1237–1242. doi: 10.1016/S1464-1909(00)00186-6
[18] Hu, M., S. Weygandt, S. Benjamin, et al., 2008: Ongoing development and testing of generalized cloud analysis package within GSI for initializing rapid refresh. Proceedings of the 13th Conference on Aviation, Range and Aerospace Meteorology, American Meteorology Society, New Orleans, 1–10.
[19] Hunter, S. M., 1996: WSR-88D radar rainfall estimation: Capabilities, limitations and potential improvements. Natl. Wea. Dig., 20, 26–38.
[20] Iacono, M. J., J. S. Delamere, E. J. Mlawer, et al., 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. Atmos., 113, D13103. doi: 10.1029/2008JD009944
[21] Jin, J. M., N. L. Miller, and N. Schlegel, 2010: Sensitivity study of four land surface schemes in the WRF model. Adv. Meteor., 167436. doi: 10.1155/2010/167436
[22] Krishnamurti, T. N., K. Ingles, S. Cocke, et al., 1984: Details of low latitude medium range numerical weather prediction using a global spectral model. II: Effect of orography and physical initialization. J. Meteor. Soc. Japan, 62, 613–649.
[23] Krishnamurti, T. N., J. S. Xue, H. S. Bedi, et al., 1991: Physical initialization for numerical weather prediction over the tropics. Tellus A, 43, 53–81. doi: 10.3402/tellusb.v43i4.15398
[24] Krishnamurti, T. N., H. S. Bedi, and K. Ingles, 1993: Physical initialization using SSM/I rain rates. Tellus A, 45, 247–269. doi: 10.3402/tellusa.v45i4.14890
[25] Krishnamurti, T. N., G. D. Rohaly, and H. S. Bedi, 1994: On the improvement of precipitation forecast skill from physical initialization. Tellus A, 46, 598–614. doi: 10.3402/tellusa.v46i5.15647
[26] Lindskog, M., K. Salonen, H. Järvinen, et al., 2004: Doppler radar wind data assimilation with HIRLAM 3DVAR. Mon. Wea. Rev., 132, 1081–1092. doi: 10.1175/1520-0493(2004)132<1081:DRWDAW>2.0.CO;2
[27] Liu, Y., Z. Li, X. Y. Cheng, et al., 2015: Comparative analysis of LD-Ⅱand ADTD lightning location data. J. Nanjing Univ. Informat. Sci. Technol., 7, 380–384. (in Chinese) doi: 10.13878/j.cnki.jnuist.2015.04.011
[28] Lynn, B. H., 2017: The usefulness and economic value of total lightning forecasts made with a dynamic lightning scheme coupled with lightning data assimilation. Wea. Forecasting, 32, 645–663. doi: 10.1175/WAF-D-16-0031.1
[29] Lynn, B. H., G. Kelman, and G. Ellrod, 2015: An evaluation of the efficacy of using observed lightning to improve convective lightning forecasts. Wea. Forecasting, 30, 405–423. doi: 10.1175/WAF-D-13-00028.1
[30] Mansell, E. R., C. L. Ziegler, and D. R. MacGorman, 2007: A lightning data assimilation technique for mesoscale forecast models. Mon. Wea. Rev., 135, 1732–1748. doi: 10.1175/MWR3387.1
[31] Marchand, M. R., and H. E. Fuelberg, 2014: Assimilation of lightning data using a nudging method involving low-level warming. Mon. Wea. Rev., 142, 4850–4871. doi: 10.1175/MWR-D-14-00076.1
[32] Milan, M., F. Amen, V. Venema, et al., 2005: Physical initialization to incorporate radar precipitation data into a numerical weather prediction model (lokal model). Proceedings of the 32nd Conference on Radar Meteorology, American Meteorological Society, Albuquerque, New Mexico, 1–5.
[33] Petersen, W. A., and S. A. Rutledge, 1998: On the relationship between cloud-to-ground lightning and convective rainfall. J. Geophys. Res. Atmos., 103, 14025–14040. doi: 10.1029/97JD02064
[34] Pu, Z. X., X. L. Li, and J. Z. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall. J. Atmos. Sci., 66, 3351–3365. doi: 10.1175/2009JAS3121.1
[35] Qie, X. S., 2012: Progresses in the atmospheric electricity researches in China during 2006–2010. Adv. Atmos. Sci., 29, 993–1005. doi: 10.1007/s00376-011-1195-0
[36] Qie, X. S., D. X. Liu, and Z. L. Sun, 2014a: Recent advances in research of lightning meteorology. J. Meteor. Res., 28, 983–1002. doi: 10.1007/s13351-014-3295-0
[37] Qie, X. S., R. P. Zhu, T. Yuan, et al., 2014b: Application of total-lightning data assimilation in a mesoscale convective system based on the WRF model. Atmos. Res., 145-–146, 255–266. doi: 10.1016/j.atmosres.2014.04.012
[38] Rico-Ramirez, M. A., I. D. Cluckie, and D. Han, 2005: Correction of the bright band using dual-polarisation radar. Atmos. Sci. Lett., 6, 40–46. doi: 10.1002/asl.89
[39] Rudlosky, S. D., and H. E. Fuelberg, 2013: Documenting storm severity in the Mid-Atlantic region using lightning and radar information. Mon. Wea. Rev., 141, 3186–3202. doi: 10.1175/MWR-D-12-00287.1
[40] Schwartz, C. S., J. S. Kain, S. J. Weiss, et al., 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263–280. doi: 10.1175/2009waf2222267.1
[41] Sheng, P. X., J. T. Mao, and J. G. Li, 2013: Atmospheric Physics. Peking University Press, Beijing, 522 pp. (in Chinese)
[42] Stefanescu, R., I. M. Navon, H. Fuelberg, et al., 2013: 1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators. Available online at https://arxiv.org/ftp/arxiv/papers/1306/1306.1884.pdf. Accessed on 13 January 2021.
[43] Sun, J. Z., and H. L. Wang, 2013: Radar data assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a squall line over the U.S. Great Plains. Mon. Wea. Rev., 141, 2245–2264. doi: 10.1175/MWR-D-12-00169.1
[44] Sušelj, K., and A. Sood, 2010: Improving the Mellor–Yamada–Janjić parameterization for wind conditions in the marine planetary boundary layer. Bound.-Lay. Meteor., 136, 301–324. doi: 10.1007/s10546-010-9502-3
[45] Thompson, G., P. R. Field, R. M. Rasmussen, et al., 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115. doi: 10.1175/2008mwr2387.1
[46] Wang, Y., Y. Yang, and C. H. Wang, 2014: Improving forecasting of strong convection by assimilating cloud-to-ground lightning data using the physical initialization method. Atmos. Res., 150, 31–41. doi: 10.1016/j.atmosres.2014.06.017
[47] Wang, Y., Y. Yang, and X. B. Qiu, 2015: Assimilating cloud-to-ground lightning data using ensemble square root filter. J. Arid Meteor., 33, 761–768. (in Chinese) doi: 10.11755/j.issn.1006-7639(2015)-05-0761
[48] Wang, Y., Y. Yang, D. X. Liu, et al., 2017: A case study of assimilating lightning-proxy relative humidity with WRF-3DVAR. Atmosphere, 8, 55. doi: 10.3390/atmos8030055
[49] Wang, Y., Y. Yang, and S. L. Jin, 2018: Evaluation of lightning forecasting based on one lightning parameterization scheme and two diagnostic methods. Atmosphere, 9, 99. doi: 10.3390/atmos9030099
[50] Williams, E. R., M. E. Weber, and R. E. Orville, 1989: The relationship between lightning type and convective state of thunderclouds. J. Geophys. Res. Atmos., 94, 13213–13220. doi: 10.1029/JD094iD11p13213
[51] Yang, Y., C. J. Qiu, and J. D. Gong, 2006: Physical initialization applied in WRF-Var for assimilation of Doppler radar data. Geophys. Res. Lett., 33, 121–132. doi: 10.1029/2006GL027656
[52] Yang, Y., C. J. Qiu, J. D. Gong, et al., 2009: The WRF 3DVar system combined with physical initialization for assimilation of Doppler radar data. Acta Meteor. Sinica, 23, 129–139.
[53] Yang, Y., Y. Wang, and K. F. Zhu, 2015: Assimilation of Chinese Doppler radar and lightning data using WRF-GSI: A case study of mesoscale convective system. Adv. Meteor., 763919. doi: 10.1155/2015/763919
[54] Zhang, C. X., Y. Q. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the Southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 3489–3513. doi: 10.1175/MWR-D-10-05091.1
[55] Zhang, R., Y. J. Zhang, L. T. Xu, et al., 2017: Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution. J. Meteor. Res., 31, 731–746. doi: 10.1007/s13351-017-6133-3